I just remembered that you can also try RANSAC, which was recently added to
scikit-learn master:
http://scikit-learn.org/dev/auto_examples/linear_model/plot_ransac.html
Mathieu
On Mon, Jan 13, 2014 at 6:45 PM, Mathieu Blondel <math...@mblondel.org>wrote:
>
> On Mon, Jan 13, 2014 at 5:09 PM, florian.wilh...@gmail.com <
> florian.wilh...@gmail.com> wrote:
>
>>
>> So setting epsilon=0 and C to a large value should result in a
>> regression in the L1 norm with almost no regularization of w, right?.
>> One thing that just crossed my mind. Would it be possible in a linear
>> SVR setting to let the norm(w) term [in the primal objective funtion]
>> be in the L1 norm in order to get some sparsity like in Lasso?
>>
>
> That's definitely possible. If your dataset is not too large, you can
> formulate the objective as a linear program (LP) and use an LP solver to
> find the solution.
>
> Mathieu
>
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